Qwen3.5

Qwen 3.5
Developer	Alibaba Cloud
Release Date	February 15, 2026
Model Sizes	0.8B, 2B, 4B, 9B, 27B (dense), 35B-A3B (MoE), 122B-A10B (MoE), 397B-A17B (MoE)
Architecture	Decoder-only Transformer
Modality	Image-Text-to-Text
Thinking	Yes (toggleable)
Context Length	262,144 (up to 1M via API)
License	Apache 2.0
Languages	201 languages and dialects
Hugging Face	Qwen 3.5
Paper	Link

Qwen3.5 is an open-weight and native vision-language foundation model series developed by Alibaba and released on February 15, 2026. It is build on a hybrid architecture using linear attention with Gated Delta Networks as well as sparse Mixture of Experts. The models support 201 languages and dialects, compared to 119 of their earlier Qwen3 model series.

Benchmarks

Results for the flagship 397B-A17B and 9b, 4B as well as 2B small models.

Benchmark	Category	397B-A17B	9B	4B	2B	Claude Opus 4.6
GPQA Diamond	Science	89.3	80.6	77.1	--	89.6
SWE-bench Verified	Coding	76.4	--	--	--	80.8
MMMU-Pro	Multimodal	79.0	70.1	66.3	50.3	73.9
MMMLU	Multilingual	88.5	81.2	76.1	63.1	91.1

Tokenizer

The Qwen3.5 tokenizer has a total of 248,077 tokens (up from 151,669 in Qwen3), out of which 248,044 stem from the BPE vocabulary size, and 33 are added tokens. Note that Qwen3.5 uses 248,320 embeddings in its embedding table, slightly more than the total token number of the tokenizer. This might be due to performance improvements (it is a multiple of 512) and leaves room for adding additional tokens in the future.

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-0.8B")

len(tokenizer)
# -> 248077

messages = [{"role": "user", "content": "Hi"}]
tokenizer.apply_chat_template(messages, enable_thinking=False, tokenize=False, add_generation_prompt=True)
# -> '<|im_start|>user\nHi<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n'

tokenizer.apply_chat_template(messages, enable_thinking=True, tokenize=False, add_generation_prompt=True)
# -> '<|im_start|>user\nHi<|im_end|>\n<|im_start|>assistant\n<think>\n'

tokenizer.apply_chat_template(messages, enable_thinking=True, tokenize=True, add_generation_prompt=True)
# -> {'input_ids': [248045, 846, 198, 12675, 248046, 198, 248045, 74455, 198, 248068, 198], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}